MegEngine/imperative/python/test/unit/test_tracing.py

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import io
from tempfile import mkstemp

import numpy as np
import pytest

from megengine import tensor
from megengine.core.ops import builtin as ops
from megengine.core.tensor import megbrain_graph as G
from megengine.core.tensor.core import apply
from megengine.core.tensor.raw_tensor import as_raw_tensor
from megengine.functional import exp, log
from megengine.jit import exclude_from_trace, trace


def test_trace():
    for symbolic in [False, True]:

        @trace(symbolic=symbolic)
        def f(x):
            op = ops.Elemwise(mode="negate")
            (y,) = apply(op, x)
            return y

        x = as_raw_tensor([1]).numpy()
        y = f.__wrapped__(as_raw_tensor(x)).numpy()

        for i in range(3):
            np.testing.assert_equal(f(as_raw_tensor(x)).numpy(), y)


def test_exclude_from_trace():
    for symbolic in [False, True]:

        @trace(symbolic=symbolic)
        def f(x):
            neg = ops.Elemwise(mode="negate")
            (x,) = apply(neg, x)
            with exclude_from_trace():
                if i % 2:
                    (x,) = apply(neg, x)
            (x,) = apply(neg, x)
            return x

        x = as_raw_tensor([1]).numpy()

        for i in range(3):
            y = f.__wrapped__(as_raw_tensor(x)).numpy()
            np.testing.assert_equal(f(as_raw_tensor(x)).numpy(), y)


def test_print_in_trace():
    for symbolic in [False]:  # cannot read value in symbolic mode

        @trace(symbolic=symbolic)
        def f(x):
            nonlocal buf
            neg = ops.Elemwise(mode="negate")
            (x,) = apply(neg, x)
            buf = x.numpy()
            (x,) = apply(neg, x)
            return x

        buf = None
        x = as_raw_tensor([1]).numpy()

        for i in range(3):
            y = f.__wrapped__(as_raw_tensor(x)).numpy()
            z = buf
            buf = None
            np.testing.assert_equal(f(as_raw_tensor(x)).numpy(), y)
            np.testing.assert_equal(z, buf)


def test_dump():
    @trace(symbolic=True, capture_as_const=True)
    def f(x):
        op = ops.Elemwise(mode="negate")
        (y,) = apply(op, x)
        return y

    x = as_raw_tensor([1]).numpy()
    y = f.__wrapped__(as_raw_tensor(x)).numpy()

    for i in range(3):
        np.testing.assert_equal(f(as_raw_tensor(x)).numpy(), y)

    file = io.BytesIO()
    f.dump(file)


def test_trace_profiler():
    for symbolic in [False, True]:

        @trace(symbolic=symbolic, profiling=True)
        def f(x):
            op = ops.Elemwise(mode="negate")
            (y,) = apply(op, x)
            return y

        x = as_raw_tensor([1]).numpy()
        y = f.__wrapped__(as_raw_tensor(x)).numpy()

        f(as_raw_tensor(x))
        f(as_raw_tensor(x))  # XXX: has to run twice

        out = f.get_profile()
        assert out.get("profiler")


@pytest.mark.skip(reason="eq_to_unit failed in inplace.cpp")
def test_goptions_div_zero():
    @trace(symbolic=True, opt_level=0)
    def f(x):
        return x / x

    @trace(symbolic=True, opt_level=1)
    def g(x):
        return x / x

    out = f(tensor(0.0))
    if out == out:
        raise ValueError("actual result should be nan")

    out = g(tensor(0.0))
    if out != out:
        raise ValueError("actual result should be 1")


@pytest.mark.skip(reason="cast to Elemwise failed in inplace.cpp")
def test_goptions_log_exp():
    @trace(symbolic=True, opt_level=0, capture_as_const=True)
    def f(x):
        return log(exp(x))

    @trace(symbolic=True, opt_level=1, capture_as_const=True)
    def g(x):
        return log(exp(x))

    f(tensor(1.0))
    _, out = mkstemp()
    f.dump(out)
    *_, outputs = G.load_comp_graph_from_file(out)
    oprs_1 = cgtools.get_oprs_seq(outputs)

    g(tensor(1.0))
    g.dump(out)
    *_, outputs = G.load_comp_graph_from_file(out)
    oprs_2 = cgtools.get_oprs_seq(outputs)

    assert len(oprs_1) - len(oprs_2) == 2


@pytest.mark.skip(reason="need cgtools to check final oprs")
def test_goptions_log_sum_exp():
    @trace(symbolic=True, opt_level=0, capture_as_const=True)
    def f(x, y):
        return log(exp(x) + exp(y))

    @trace(symbolic=True, opt_level=1, capture_as_const=True)
    def g(x, y):
        return log(exp(x) + exp(y))

    f(tensor(1.0), tensor(2.0))
    _, out = mkstemp()
    f.dump(out)
    *_, outputs = G.load_comp_graph_from_file(out)
    oprs_1 = cgtools.get_oprs_seq(outputs)

    g(tensor(1.0), tensor(2.0))
    g.dump(out)
    *_, outputs = G.load_comp_graph_from_file(out)
    oprs_2 = cgtools.get_oprs_seq(outputs)

    assert len(oprs_1) - len(oprs_2) == 2


@pytest.mark.skip(reason="need cgtools to check computing input dtype")
def test_optimize_for_inference():
    @trace(symbolic=True, capture_as_const=True)
    def f(x):
        return exp(x)

    _, out = mkstemp()
    f(tensor(5.0))
    f.dump(out, optimize_for_inference=True, optimize_options={"enable_io16xc32": True})

    res = G.load_comp_graph_from_file(out)
    computing_input = res.output_vars_list[0].owner.inputs[0]
    assert computing_input.dtype == np.float16